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Daptomycin (Subrtova 2026)

Source: vignettes/articles/Subrtova_2026_daptomycin.Rmd
Subrtova_2026_daptomycin.Rmd

Model and source

  • Citation: Subrtova P, Rozsivalova P, Halvova P, Malakova J, Paterova P, Ryskova L, Maly J, Michalek P, Slanar O, Sima M. Population pharmacokinetic model and dosing nomogram for daptomycin in adult patients with serious Gram-positive infections: emphasizing the role of loading doses and renal function-based adjustment. Antimicrob Agents Chemother. 2026;70(3):e01532-25. doi:10.1128/aac.01532-25.

  • Description: One-compartment population PK model with linear elimination for intravenous daptomycin in adult patients with serious Gram-positive infections (infective endocarditis, bone and joint infection, sepsis, catheter-associated infection), developed from routine therapeutic drug monitoring data at a single Czech centre (143 serum concentrations from 31 patients, 2022-2025; patients on renal replacement therapy excluded). Clearance is scaled by a power function of the time-varying CKD-EPI 2021 estimated glomerular filtration rate normalised to 90 mL/min: CL = 0.69 * (eGFR/90)^0.40 L/h. eGFR was the only retained covariate – age, height, total / ideal / adjusted body weight, lean body mass, body surface area, sex, serum creatinine, Cockcroft-Gault creatinine clearance and serum urea were all tested and rejected, and the authors argue the absence of a body-size effect is genuine rather than a sample-size artefact. Inter-individual variability is log-normal on the volume of distribution, on clearance, and also on the eGFR power exponent itself: eGFR entered the Monolix model as a time-varying regressor rather than as a built-in covariate, which makes the exponent an ordinary individual parameter carrying its own random effect. Residual variability is proportional. The paper converts the clearance-eGFR relationship into a once-daily maintenance-dose nomogram targeting AUC24 = 832 mgh/L at MIC 1 mg/L and 665.5 mgh/L at MIC 0.5 mg/L (the midpoints of the 666-998 and 333-998 mg*h/L therapeutic windows), with an initial loading dose of 1.36 times the maintenance dose to offset the day-1 accumulation shortfall.

  • Article: https://doi.org/10.1128/aac.01532-25 (Antimicrob Agents Chemother 2026;70(3):e01532-25, open access CC-BY)

  • Supplement (Table S1, Fig S1-S5): aac.01532-25-s0001.docx, retrieved from the EuropePMC supplementary-files endpoint for PMC12959151. Table S1 is the model-building OFV trail and independently confirms the structural, error and covariate model selections used here; Figures S1-S5 are diagnostic plots. The supplement carries no parameter value that is absent from the main text.

Subrtova and colleagues fitted a one-compartment model with linear elimination to 143 routine therapeutic-drug-monitoring serum concentrations from 31 adults treated with intravenous daptomycin for serious Gram-positive infections. The CKD-EPI 2021 estimated glomerular filtration rate, entered as a time-varying regressor, was the only covariate retained; every body-size descriptor was tested and rejected. The paper then converts the clearance-eGFR relationship into a once-daily dosing nomogram with a loading dose.

Population

  • Species: human
  • Subjects / observations: 31 patients, 143 serum concentrations
  • Age: median 63 years (range 29-93 years)
  • Weight: median 84 kg (range 48-124 kg)
  • Female: 22.6%
  • Renal function: eGFR CKD-EPI 2021 median 81.6 mL/min (IQR 58.9-98.9, range 16.3-130.1); eGFR CKD-EPI 2012 median 73.5 mL/min (range 14.9-132.1); Cockcroft-Gault creatinine clearance median 54.2 mL/min (range 15.2-176.0). Patients receiving renal replacement support were EXCLUDED, so the model carries no information about dialysis or CRRT.
  • Dosing: 350-1,000 mg (median 750 mg), i.e. 5-13 mg/kg (median 10 mg/kg), every 24 or 48 h as a 30- or 60-min intravenous infusion; the initial regimen was set by the attending physician and subsequently adjusted by therapeutic drug monitoring.
  • Region: Czech Republic (single centre: University Hospital Hradec Kralove)

Baseline demographics are Subrtova 2026 Table 1. Patients receiving renal replacement support were excluded, so the model carries no information about dialysis or CRRT. The same metadata is available programmatically via readModelDb("Subrtova_2026_daptomycin")()$population.

Source trace

Equation / parameter Value Source location
Vd = Vd_pop (no covariate on volume) n/a Table 2, Fixed effects block
CL = CL_pop * (eGFR/90)^beta_CL_eGFR n/a Table 2, Fixed effects block
lvc (Vd_pop) 11.47 L (R.S.E. 6.88%) Table 2; Results: “daptomycin Vd and CL were 11.47 L and 0.69 L/h”
lcl (CL_pop at eGFR 90 mL/min) 0.69 L/h (R.S.E. 5.13%) Table 2; Results, same sentence
le_crcl_cl (beta_CL_eGFR) 0.40 (R.S.E. 32.40%) Table 2, Fixed effects block
etalvc (BSV on Vd) 18.0% (R.S.E. 29.8%) Table 2, “Between subject variability (%)”
etalcl (BSV on CL) 20.0% (R.S.E. 22.3%) Table 2, “Between subject variability (%)”
etale_crcl_cl (BSV on beta_CL_eGFR) 39.0% (R.S.E. 29.8%) Table 2, “Between subject variability (%)”
propSd (proportional error) 0.28 (R.S.E. 7.72%) Table 2, “Error model parameter”
One compartment, linear elimination, proportional RUV n/a Results, “Population pharmacokinetic analysis”, first paragraph; Table S1 (1-cmt 1st-order OFV 1201.98 beats 2-cmt at max R.S.E. 7.76e17 and saturation at 1202.01; proportional error OFV 1201.98 beats constant 1269.52 and combined 1202.05)
Total-CL parameterisation rather than split renal / non-renal CL n/a Table S1, “Time-varying covariate model”: CL = CL_pop * (eGFR2021/90)^beta (OFV 1173.24, max R.S.E. 32.4%) selected over CL = CLnr_pop + CLr_pop * (eGFR2021/90)^beta (OFV 1178.16, max R.S.E. 1.37e8, shrinkage 100%)
Intravenous infusion, 30 or 60 min, no absorption term n/a Methods, “Study design”; Results, “Study population”
Reference eGFR 90 mL/min n/a Results: “an individual with normal renal function status (eGFR = 90 mL/min)”
Nomogram AUC24 targets 832 and 665.5 mg*h/L n/a Discussion: midpoints of the 666-998 and 333-998 mg*h/L windows
Cmin-to-AUC24 regression AUC24 = 27.39*Cmin + 340.4 r2 = 0.9552, p < 0.0001 Figure 1, equation printed inside the plot panel
Toxicity-threshold conversion Cmin 24 mg/L -> AUC24 998 mg*h/L 997.76 Figure 1 equation evaluated at Cmin = 24; quoted as 998 in the Discussion
Loading dose factor 1.36 n/a Results, Monte Carlo simulations; Table 3 “Median AUC24 ratio (day 7 vs day 1)”
Three random effects (i.e. the BSV on beta_CL_eGFR is real) p = 7 Table S1, final-model row: OFV 1173.24 / AIC 1187.24 / BIC 1197.28 inverted for the parameter count; see “Confirming the random-effect count from Table S1” below

Published reference values used as validation targets below:

Quantity Published value Source location
CL and t1/2 at eGFR 30 mL/min 0.44 L/h, 18.1 h Results, “Population pharmacokinetic analysis”
CL and t1/2 at eGFR 90 mL/min 0.69 L/h, 11.5 h Results, same paragraph
CL and t1/2 at eGFR 130 mL/min 0.80 L/h, 9.9 h Results, same paragraph
Day-7 AUC24, SmPC 6 mg/kg q24h 747.8 mg*h/L (90% CI 670.6-834.3) Table 3, “SmPC” column
Day-7 AUC24, nomogram MIC 1 mg/L 821 mg*h/L (751.6-906.2) Table 3
Day-7 AUC24, nomogram MIC 0.5 mg/L 658.4 mg*h/L (601.8-717.5) Table 3
Day-1 AUC24, nomogram + LD, MIC 1 mg/L 825.9 mg*h/L (778.1-872.5) Table 3
Day-1 AUC24, nomogram + LD, MIC 0.5 mg/L 660.7 mg*h/L (622.3-699.3) Table 3
Day-7 / day-1 AUC24 ratio, no loading dose 1.34-1.37 Table 3, bottom row

Structural parameters as packaged 

th <- setNames(mod$iniDf$est, mod$iniDf$name)
vd_pop  <- exp(th[["lvc"]])
cl_pop  <- exp(th[["lcl"]])
beta_cl <- exp(th[["le_crcl_cl"]])

# Typical clearance as a function of eGFR (Subrtova 2026 Table 2, Fixed effects).
cl_typical <- function(crcl) cl_pop * (crcl / 90)^beta_cl

c(Vd_pop_L = vd_pop, CL_pop_L_per_h = cl_pop, beta_CL_eGFR = beta_cl)
#>       Vd_pop_L CL_pop_L_per_h   beta_CL_eGFR 
#>          11.47           0.69           0.40

# Guard: the packaged values are the published ones.
stopifnot(
  all.equal(vd_pop, 11.47, tolerance = 1e-6),
  all.equal(cl_pop, 0.69, tolerance = 1e-6),
  all.equal(beta_cl, 0.40, tolerance = 1e-6)
)

Confirming the random-effect count from Table S1

The single structural decision in this extraction that is not stated in words by the paper is whether Table 2’s third “Between subject variability” row – 39.0% on beta_CL_eGFR – is a genuine third random effect or a mislabelled duplicate. A random effect on a covariate coefficient is unusual enough to be worth proving rather than assuming, and the supplement proves it.

Monolix reports AIC = OFV + 2p and BIC = OFV + p*log(N_subjects), with N = 31 here. Supplementary Table S1 gives OFV, AIC and BIC for every candidate model, which over-determines p: each row yields an independent estimate from the AIC and from the BIC, and they must agree at an integer.

tableS1 <- tibble::tribble(
  ~model,                                  ~OFV,     ~AIC,     ~BIC,
  "1-cmt, 1st order (base)",                1201.98,  1211.98,  1219.15,
  "2-cmt, 1st order",                       1185.58,  1203.58,  1216.49,
  "1-cmt, saturable elimination",           1202.01,  1216.01,  1226.05,
  "1-cmt, constant error",                  1269.52,  1279.52,  1286.69,
  "1-cmt, combined error",                  1202.05,  1214.05,  1222.66,
  "CL = CLnr + CLr*(eGFR/90)^beta",         1178.16,  1196.16,  1209.06,
  "CL = CL_pop*(eGFR/90)^beta (FINAL)",     1173.24,  1187.24,  1197.28
)

n_subjects <- 31L      # Results, Study population: "Thirty-one patients"

param_count <- tableS1 |>
  mutate(
    p_from_aic = (AIC - OFV) / 2,
    p_from_bic = (BIC - OFV) / log(n_subjects)
  )

# The two routes must agree, and must land on an integer. If Monolix's
# information-criterion convention were anything other than assumed, this would
# not close on every one of the seven rows.
stopifnot(
  nrow(param_count) == 7L,
  max(abs(param_count$p_from_aic - round(param_count$p_from_aic))) < 1e-8,
  max(abs(param_count$p_from_bic - round(param_count$p_from_bic))) < 0.005,
  all(round(param_count$p_from_aic) == round(param_count$p_from_bic))
)

param_count |>
  mutate(p = round(p_from_aic),
         across(c(p_from_aic, p_from_bic), \(x) round(x, 3))) |>
  rename("Table S1 model" = model, "p (from AIC)" = p_from_aic,
         "p (from BIC)" = p_from_bic, "Estimated parameters" = p) |>
  knitr::kable(
    caption = paste(
      "Number of estimated parameters recovered from Subrtova 2026 Table S1 by",
      "inverting Monolix's AIC = OFV + 2p and BIC = OFV + p*log(31). The AIC and",
      "BIC routes are independent and agree on an integer for all seven rows."
    ),
    align = c("l", rep("r", 5))
  )
Number of estimated parameters recovered from Subrtova 2026 Table S1 by inverting Monolix’s AIC = OFV + 2p and BIC = OFV + p*log(31). The AIC and BIC routes are independent and agree on an integer for all seven rows.
Table S1 model OFV AIC BIC p (from AIC) p (from BIC) Estimated parameters
1-cmt, 1st order (base) 1201.98 1211.98 1219.15 5 5.000 5
2-cmt, 1st order 1185.58 1203.58 1216.49 9 9.001 9
1-cmt, saturable elimination 1202.01 1216.01 1226.05 7 7.001 7
1-cmt, constant error 1269.52 1279.52 1286.69 5 5.000 5
1-cmt, combined error 1202.05 1214.05 1222.66 6 6.002 6
CL = CLnr + CLr*(eGFR/90)^beta 1178.16 1196.16 1209.06 9 8.998 9
CL = CL_pop*(eGFR/90)^beta (FINAL) 1173.24 1187.24 1197.28 7 7.001 7

The final model returns p = 7. The fixed effects account for three (Vd_pop, CL_pop, beta_CL_eGFR) and the proportional error for one, leaving three random effects – so the 39.0% on beta_CL_eGFR is a real omega and etale_crcl_cl belongs in the packaged model. Six would be the count if that row were a duplicate. The base model on the same arithmetic returns p = 5 (Vd_pop, CL_pop, two omegas, one residual), consistent with the final model adding exactly one fixed effect and one random effect when eGFR entered.

p_final <- round(param_count$p_from_aic[param_count$model ==
                                          "CL = CL_pop*(eGFR/90)^beta (FINAL)"])
p_base  <- round(param_count$p_from_aic[param_count$model ==
                                          "1-cmt, 1st order (base)"])

n_omega_implied <- p_final - 3L - 1L    # minus 3 fixed effects, minus 1 residual
n_omega_packaged <- sum(mod$iniDf$neta1 > 0 & mod$iniDf$neta1 == mod$iniDf$neta2,
                        na.rm = TRUE)

c(p_base = p_base, p_final = p_final,
  omegas_implied_by_TableS1 = n_omega_implied,
  omegas_in_packaged_model  = n_omega_packaged)
#>                    p_base                   p_final omegas_implied_by_TableS1 
#>                         5                         7                         3 
#>  omegas_in_packaged_model 
#>                         3

stopifnot(
  p_base  == 5L,
  p_final == 7L,
  n_omega_implied == 3L,
  # The packaged model must carry exactly the random effects Table S1 pays for.
  n_omega_packaged == n_omega_implied
)

Deterministic replication: the clearance / half-life ladder (Figure 2)

The Results paragraph gives three (eGFR, CL, t1/2) triples. Both sides of this check use the same parameter values – there is no per-subject sampling – so the comparison is a pure arithmetic identity and is asserted tightly.

egfr_ladder <- c(30, 90, 130)
ladder_dose <- 750            # mg, the cohort median dose (Results, Study population)
inf_dur     <- 0.5            # h, 30-min infusion

# Fine early sampling resolves the infusion ramp; the decay phase is exact under
# PKNCA's linear-up / log-down trapezoid for a mono-exponential. The window stops
# at 72 h so the last concentration stays above the assay LLOQ of 0.32 mg/L even
# for the fastest-clearing arm (Methods, Bioanalytical assay).
ladder_times <- sort(unique(c(
  seq(0, 0.5, by = 0.05), seq(0.75, 4, by = 0.25), seq(5, 24, by = 1),
  seq(26, 72, by = 2)
)))

ladder_subj <- tibble(id = seq_along(egfr_ladder), CRCL = egfr_ladder) |>
  mutate(arm = sprintf("eGFR %d mL/min", CRCL))

ladder_dosing <- ladder_subj |>
  mutate(time = 0, amt = ladder_dose, evid = 1L, cmt = "central",
         rate = ladder_dose / inf_dur)
ladder_obs <- ladder_subj |>
  crossing(time = ladder_times) |>
  mutate(amt = NA_real_, evid = 0L, cmt = "central", rate = NA_real_)

ladder_ev <- bind_rows(ladder_dosing, ladder_obs) |>
  arrange(id, time, desc(evid)) |>
  select(id, time, amt, evid, cmt, rate, CRCL, arm) |>
  as.data.frame()
# zeroRe() alone is not enough once a stochastic solve has run in the same
# session -- rxode2 retains the previous omega. `omega = NA` is the explicit
# no-random-effects sentinel.
mod_typ <- rxode2::zeroRe(mod)
ladder_sim <- rxode2::rxSolve(mod_typ, events = ladder_ev,
                              keep = c("arm"), omega = NA) |>
  as.data.frame()
#> Warning: multi-subject simulation without without 'omega'

# Mechanical guard that the typical-value solve really is typical: exactly one
# cl per eGFR level (three subjects, three distinct values, no eta noise).
stopifnot(n_distinct(round(ladder_sim$cl, 10)) == length(egfr_ladder))
fig2 <- tibble(CRCL = seq(15, 135, by = 1)) |>
  mutate(CL = cl_typical(CRCL), `t1/2` = log(2) * vd_pop / CL) |>
  pivot_longer(c(CL, `t1/2`), names_to = "quantity", values_to = "value")

fig2_pts <- tibble(
  CRCL     = egfr_ladder,
  CL       = c(0.44, 0.69, 0.80),
  `t1/2`   = c(18.1, 11.5, 9.9)
) |>
  pivot_longer(c(CL, `t1/2`), names_to = "quantity", values_to = "value")

ggplot(fig2, aes(CRCL, value)) +
  geom_line(linewidth = 0.8) +
  geom_point(data = fig2_pts, colour = "firebrick", size = 2.5) +
  facet_wrap(~quantity, scales = "free_y",
             labeller = as_labeller(c(CL = "CL (L/h)", "t1/2" = "t1/2 (h)"))) +
  labs(x = "eGFR CKD-EPI 2021 (mL/min)", y = NULL,
       caption = "Line: packaged model. Points: values quoted in Subrtova 2026 Results.") +
  theme_bw()
Replicates Figure 2 of Subrtova 2026: typical daptomycin clearance and elimination half-life against CKD-EPI 2021 eGFR. Points mark the three (eGFR, CL, t1/2) triples quoted in the Results.

Replicates Figure 2 of Subrtova 2026: typical daptomycin clearance and elimination half-life against CKD-EPI 2021 eGFR. Points mark the three (eGFR, CL, t1/2) triples quoted in the Results.

PKNCA validation of the ladder 

ladder_nca <- ladder_sim |>
  filter(!is.na(Cc)) |>
  select(id, time, Cc, arm)

ladder_nca <- bind_rows(
  ladder_nca,
  ladder_nca |> distinct(id, arm) |> mutate(time = 0, Cc = 0)
) |>
  distinct(id, arm, time, .keep_all = TRUE) |>
  arrange(id, arm, time)

ladder_dose_df <- ladder_ev |>
  filter(evid == 1) |>
  select(id, time, amt, arm) |>
  mutate(route = "intravascular", duration = inf_dur)

conc_ladder <- PKNCA::PKNCAconc(ladder_nca, Cc ~ time | arm + id,
                                concu = "mg/L", timeu = "h")
dose_ladder <- PKNCA::PKNCAdose(ladder_dose_df, amt ~ time | arm + id,
                                doseu = "mg")
#> Found column named route, using it for the attribute of the same name.
#> Found column named duration, using it for the attribute of the same name.

ladder_intervals <- data.frame(
  start = 0, end = Inf,
  cmax = TRUE, tmax = TRUE, aucinf.obs = TRUE,
  half.life = TRUE, cl.obs = TRUE, vz.obs = TRUE
)

ladder_res <- PKNCA::pk.nca(
  PKNCA::PKNCAdata(conc_ladder, dose_ladder, intervals = ladder_intervals)
)

PKNCA::cl.obs is dose / AUCinf and here carries units of mg / (mg/L * h) = L/h, directly comparable to the published clearances. vz.obs is cl.obs / lambda.z, which for a one-compartment model equals Vd exactly.

published_ladder <- tibble::tribble(
  ~arm,                  ~half.life, ~cl.obs, ~vz.obs,
  "eGFR 30 mL/min",      18.1,       0.44,    11.47,
  "eGFR 90 mL/min",      11.5,       0.69,    11.47,
  "eGFR 130 mL/min",      9.9,       0.80,    11.47
)

cmp_ladder <- nlmixr2lib::ncaComparisonTable(
  simulated = ladder_res,
  reference = published_ladder,
  by        = "arm",
  units     = c(half.life = "h", cl.obs = "L/h", vz.obs = "L"),
  tolerance_pct = 20
)

knitr::kable(
  cmp_ladder,
  caption = paste(
    "Simulated (PKNCA on the packaged model, typical values) vs published",
    "clearance, half-life and volume at three eGFR levels.",
    "* marks a difference above 20%."
  ),
  align = c("l", "l", "r", "r", "r")
)
Simulated (PKNCA on the packaged model, typical values) vs published clearance, half-life and volume at three eGFR levels. * marks a difference above 20%.
NCA parameter arm Reference Simulated % diff
t½ (h) eGFR 30 mL/min 18.1 17.9 -1.2%
t½ (h) eGFR 90 mL/min 11.5 11.5 +0.2%
t½ (h) eGFR 130 mL/min 9.9 9.95 +0.5%
CL/F (L/h) eGFR 30 mL/min 0.44 0.445 +1.1%
CL/F (L/h) eGFR 90 mL/min 0.69 0.69 +0.0%
CL/F (L/h) eGFR 130 mL/min 0.8 0.799 -0.1%
Vz/F (L) eGFR 30 mL/min 11.5 11.5 +0.0%
Vz/F (L) eGFR 90 mL/min 11.5 11.5 +0.0%
Vz/F (L) eGFR 130 mL/min 11.5 11.5 +0.0% 
# Both sides are the same deterministic arithmetic, so the only slack is the
# paper's own two-significant-figure rounding of CL (0.44, 0.69, 0.80) and
# three-figure rounding of t1/2. Assert tightly.
ladder_chk <- as.data.frame(ladder_res$result) |>
  filter(PPTESTCD %in% c("half.life", "cl.obs", "vz.obs")) |>
  select(arm, PPTESTCD, PPORRES) |>
  pivot_wider(names_from = PPTESTCD, values_from = PPORRES) |>
  left_join(published_ladder, by = "arm", suffix = c("_sim", "_pub")) |>
  mutate(
    d_hl = 100 * (half.life_sim - half.life_pub) / half.life_pub,
    d_cl = 100 * (cl.obs_sim - cl.obs_pub) / cl.obs_pub,
    d_vz = 100 * (vz.obs_sim - vz.obs_pub) / vz.obs_pub
  )

stopifnot(
  nrow(ladder_chk) == length(egfr_ladder),
  max(abs(ladder_chk$d_cl)) < 1.5,   # rounding of 0.4446 -> 0.44 is 1.05%
  max(abs(ladder_chk$d_hl)) < 1.5,
  # Vz is not rounding-limited: the paper's Vd is exact at 11.47 and PKNCA
  # recovers it from cl.obs / lambda.z to floating-point accuracy. Achieved
  # 1e-4%, so this is asserted at 0.01% rather than at a nominal 0.5%.
  max(abs(ladder_chk$d_vz)) < 0.01
)
ladder_chk |>
  select(arm, d_cl, d_hl, d_vz) |>
  mutate(across(where(is.numeric), \(x) round(x, 3)))
#> # A tibble: 3 × 4
#>   arm               d_cl   d_hl  d_vz
#>   <chr>            <dbl>  <dbl> <dbl>
#> 1 eGFR 130 mL/min -0.083  0.467     0
#> 2 eGFR 30 mL/min   1.05  -1.21      0
#> 3 eGFR 90 mL/min   0      0.194     0

The dosing nomogram (Figure 4)

The nomogram inverts the clearance-eGFR relationship: a once-daily maintenance dose that puts a typical patient at a target steady-state exposure is MD = AUC24_target * CL_typical(eGFR). The Discussion fixes the targets as the midpoints of the therapeutic windows – 832 mgh/L for MIC 1 mg/L (window 666-998) and 665.5 mgh/L for MIC 0.5 mg/L (window 333-998), where 998 mg*h/L is the authors’ AUC24 equivalent of the Cmin 24 mg/L toxicity threshold.

auc_target <- c(`MIC 1 mg/L` = 832, `MIC 0.5 mg/L` = 665.5)
ld_factor  <- 1.36     # Results / Discussion: loading dose = 1.36 x maintenance dose

nomogram_dose <- function(crcl, target) target * cl_typical(crcl)
tibble(CRCL = seq(15, 135, by = 1)) |>
  mutate(`MIC 1 mg/L`   = nomogram_dose(CRCL, auc_target[["MIC 1 mg/L"]]),
         `MIC 0.5 mg/L` = nomogram_dose(CRCL, auc_target[["MIC 0.5 mg/L"]])) |>
  pivot_longer(-CRCL, names_to = "MIC", values_to = "dose") |>
  ggplot(aes(CRCL, dose, colour = MIC)) +
  geom_line(linewidth = 0.8) +
  scale_colour_manual(values = c("MIC 1 mg/L" = "black", "MIC 0.5 mg/L" = "firebrick")) +
  labs(x = "eGFR CKD-EPI 2021 (mL/min)",
       y = "Maintenance dose (mg once daily, 30-min infusion)") +
  theme_bw() + theme(legend.position = "top")
Replicates Figure 4 of Subrtova 2026: once-daily maintenance dose from the CKD-EPI 2021 eGFR, at MIC 1 mg/L (black) and MIC 0.5 mg/L (red).

Replicates Figure 4 of Subrtova 2026: once-daily maintenance dose from the CKD-EPI 2021 eGFR, at MIC 1 mg/L (black) and MIC 0.5 mg/L (red).

Figure 4 is an independent answer key for two quantities that appear nowhere else as numbers: the AUC24 targets 832 and 665.5 mg*h/L, which are stated only in the Discussion prose, and the eGFR normalisation constant of 90 mL/min. The published curves are read off the plotted figure at three eGFR values below; a read-off carries roughly +/- 2% digitisation error, so the check is asserted at 2.5%. Any error in either target, or a normalisation other than 90 mL/min, would move the computed curve by far more than that.

# Doses read off Subrtova 2026 Figure 4 (the plotted curves; the paper tabulates
# no nomogram doses). Provenance: figure digitisation, not paper text.
nomogram_readoff <- tibble::tribble(
  ~CRCL, ~`MIC 1 mg/L`, ~`MIC 0.5 mg/L`,
  20,    312,           250,
  60,    490,           392,
  130,   665,           532
)

nomo_chk <- nomogram_readoff |>
  pivot_longer(-CRCL, names_to = "MIC", values_to = "dose_fig") |>
  mutate(
    dose_model = nomogram_dose(CRCL, unname(auc_target[MIC])),
    pct_diff   = 100 * (dose_model - dose_fig) / dose_fig
  )

# Achieved max 0.8%, comfortably inside plausible read-off error; asserted at
# 2.5% to leave room for the digitisation while still failing on any real
# transcription error (a wrong AUC24 target or a normalisation other than
# 90 mL/min moves these by tens of percent).
stopifnot(nrow(nomo_chk) == 6, max(abs(nomo_chk$pct_diff)) < 2.5)

nomo_chk |>
  mutate(across(c(dose_model, pct_diff), \(x) round(x, 1))) |>
  rename(
    "eGFR (mL/min)"          = CRCL,
    "MIC"                    = MIC,
    "Figure 4 read-off (mg)" = dose_fig,
    "Packaged model (mg)"    = dose_model,
    "% difference"           = pct_diff
  ) |>
  knitr::kable(
    caption = paste(
      "Maintenance dose computed from the packaged model against doses read off",
      "Subrtova 2026 Figure 4. Agreement confirms the AUC24 targets (832 and",
      "665.5 mg*h/L) and the 90 mL/min eGFR normalisation."
    ),
    align = c("r", "l", "r", "r", "r")
  )
Maintenance dose computed from the packaged model against doses read off Subrtova 2026 Figure 4. Agreement confirms the AUC24 targets (832 and 665.5 mg*h/L) and the 90 mL/min eGFR normalisation.
eGFR (mL/min) MIC Figure 4 read-off (mg) Packaged model (mg) % difference
20 MIC 1 mg/L 312 314.5 0.8
20 MIC 0.5 mg/L 250 251.6 0.6
60 MIC 1 mg/L 490 488.1 -0.4
60 MIC 0.5 mg/L 392 390.4 -0.4
130 MIC 1 mg/L 665 665.0 0.0
130 MIC 0.5 mg/L 532 532.0 0.0

Virtual cohort

The original TDM records are not public, and the paper’s Monte Carlo simulations resampled its own 31 patients. This vignette instead builds a 200-subject virtual cohort whose weight and eGFR marginals match Table 1 (log-normal, matched on the reported median and interquartile range, truncated to the reported range). Weight and eGFR are drawn independently because the paper reports no correlation between them; weight enters only through the weight-based SmPC arm, never through the model.

set.seed(20260126)
n_sub <- 200L

# sdlog implied by the reported IQR of a log-normal
sdlog_from_iqr <- function(q1, q3) log(q3 / q1) / (2 * stats::qnorm(0.75))

cohort <- tibble(
  id   = seq_len(n_sub),
  WT   = pmin(pmax(stats::rlnorm(n_sub, log(84),   sdlog_from_iqr(73,   92)),   48),   124),
  CRCL = pmin(pmax(stats::rlnorm(n_sub, log(81.6), sdlog_from_iqr(58.9, 98.9)), 16.3), 130.1)
)

summarise(cohort,
          WT_median = median(WT), WT_q1 = quantile(WT, .25), WT_q3 = quantile(WT, .75),
          CRCL_median = median(CRCL), CRCL_q1 = quantile(CRCL, .25),
          CRCL_q3 = quantile(CRCL, .75)) |>
  round(1)
#> # A tibble: 1 × 6
#>   WT_median WT_q1 WT_q3 CRCL_median CRCL_q1 CRCL_q3
#>       <dbl> <dbl> <dbl>       <dbl>   <dbl>   <dbl>
#> 1      83.2  74.6  93.8          82    63.8    102.

Simulation of the four dosing strategies (Figure 3)

Five arms are simulated: the SmPC weight-based regimen, and the eGFR nomogram at each MIC with and without the loading dose. Table 3’s “real dosage used” column cannot be reproduced – those doses were chosen patient-by-patient by the attending physician and then adjusted by TDM.

Every arm is solved from the same covariate table with the RNG reseeded immediately before each rxSolve(), so the arms share their between-subject random effects (common random numbers). Cross-arm comparisons are therefore paired and are not confounded by Monte Carlo noise.

dose_times <- seq(0, 144, by = 24)      # seven once-daily doses; day 7 = [144, 168]
day_grid   <- c(0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.75, 1, 1.5, 2, 3, 4, 6, 8,
                10, 12, 16, 20)
obs_times  <- sort(unique(c(as.vector(outer(day_grid, dose_times, "+")), 168)))

make_arm_events <- function(label, md_vec, ld_vec) {
  dosing <- cohort |>
    mutate(md = md_vec, ld = ld_vec) |>
    crossing(time = dose_times) |>
    mutate(amt = if_else(time == 0, ld, md), evid = 1L, cmt = "central",
           rate = amt / inf_dur)
  obs <- cohort |>
    crossing(time = obs_times) |>
    mutate(amt = NA_real_, evid = 0L, cmt = "central", rate = NA_real_)
  bind_rows(dosing, obs) |>
    mutate(arm = label) |>
    arrange(id, time, desc(evid)) |>
    select(id, time, amt, evid, cmt, rate, WT, CRCL, arm) |>
    as.data.frame()
}

md_mic1   <- nomogram_dose(cohort$CRCL, auc_target[["MIC 1 mg/L"]])
md_mic05  <- nomogram_dose(cohort$CRCL, auc_target[["MIC 0.5 mg/L"]])
smpc_dose <- 6 * cohort$WT                      # SmPC 6 mg/kg once daily

arm_events <- list(
  make_arm_events("SmPC 6 mg/kg",        smpc_dose, smpc_dose),
  make_arm_events("Nomogram MIC 1",      md_mic1,   md_mic1),
  make_arm_events("Nomogram + LD MIC 1", md_mic1,   ld_factor * md_mic1),
  make_arm_events("Nomogram MIC 0.5",    md_mic05,  md_mic05),
  make_arm_events("Nomogram + LD MIC 0.5", md_mic05, ld_factor * md_mic05)
)

# Disjoint (id, time, evid) keys within each arm's own event table.
stopifnot(vapply(arm_events,
                 function(e) !anyDuplicated(e[, c("id", "time", "evid")]),
                 logical(1)))
solve_arm <- function(ev, seed = 20260126L) {
  # Common random numbers. rxode2 draws its random effects from its own
  # internal generator, so base::set.seed() alone does NOT make the etas
  # reproducible across calls -- rxode2::rxSetSeed() is the documented control.
  rxode2::rxSetSeed(seed)
  set.seed(seed)
  rxode2::rxSolve(mod, events = ev, keep = c("WT", "arm")) |>
    as.data.frame()
}

sim <- bind_rows(lapply(arm_events, solve_arm))

# Common-random-numbers guard: an id's clearance must be identical in all five
# arms (same covariates, same etas). If this fails, every paired cross-arm
# comparison below is measuring Monte Carlo noise instead of the regimen.
stopifnot(
  nrow(distinct(sim, id, cl = round(cl, 10))) == n_sub,
  all(sim$Cc >= 0, na.rm = TRUE)
)
sim |>
  filter(!is.na(Cc)) |>
  group_by(arm, time) |>
  summarise(Q05 = quantile(Cc, 0.05), Q50 = median(Cc),
            Q95 = quantile(Cc, 0.95), .groups = "drop") |>
  mutate(arm = factor(arm, levels = c(
    "SmPC 6 mg/kg", "Nomogram MIC 1", "Nomogram + LD MIC 1",
    "Nomogram MIC 0.5", "Nomogram + LD MIC 0.5"))) |>
  ggplot(aes(time, Q50)) +
  geom_ribbon(aes(ymin = Q05, ymax = Q95), alpha = 0.25) +
  geom_line(linewidth = 0.6) +
  facet_wrap(~arm) +
  labs(x = "Time (h)", y = "Daptomycin serum concentration (mg/L)") +
  theme_bw()
Replicates panels B-F of Figure 3 of Subrtova 2026: simulated daptomycin serum concentration-time profiles over seven days. Line = median, band = 5th-95th percentile of the 200-subject virtual cohort.

Replicates panels B-F of Figure 3 of Subrtova 2026: simulated daptomycin serum concentration-time profiles over seven days. Line = median, band = 5th-95th percentile of the 200-subject virtual cohort.

PKNCA validation: day-1 and day-7 exposure 

sim_nca <- sim |>
  filter(!is.na(Cc)) |>
  select(id, time, Cc, arm)

sim_nca <- bind_rows(
  sim_nca,
  sim_nca |> distinct(id, arm) |> mutate(time = 0, Cc = 0)
) |>
  distinct(id, arm, time, .keep_all = TRUE) |>
  arrange(arm, id, time)

dose_df <- bind_rows(arm_events) |>
  filter(evid == 1) |>
  select(id, time, amt, arm) |>
  mutate(route = "intravascular", duration = inf_dur)

conc_obj <- PKNCA::PKNCAconc(sim_nca, Cc ~ time | arm + id,
                             concu = "mg/L", timeu = "h")
dose_obj <- PKNCA::PKNCAdose(dose_df, amt ~ time | arm + id, doseu = "mg")
#> Found column named route, using it for the attribute of the same name.
#> Found column named duration, using it for the attribute of the same name.

# Day 1 = [0, 24]; day 7 = [144, 168], the interval after the seventh dose.
intervals <- data.frame(
  start   = c(0, 144),
  end     = c(24, 168),
  auclast = TRUE, cmax = TRUE, cmin = TRUE
)

nca_res <- PKNCA::pk.nca(PKNCA::PKNCAdata(conc_obj, dose_obj, intervals = intervals))

nca_tbl <- as.data.frame(nca_res$result) |>
  filter(PPTESTCD %in% c("auclast", "cmax", "cmin")) |>
  mutate(day = if_else(start == 0, "day1", "day7")) |>
  select(arm, id, day, PPTESTCD, PPORRES) |>
  pivot_wider(names_from = c(PPTESTCD, day), values_from = PPORRES)

Per-subject steady-state identity: AUC0-tau = dose / CL

At steady state a one-compartment model satisfies AUC0-tau = dose / CL exactly. Both sides use the same drawn parameters for the same subject, so the residual is pure numerical error plus the small shortfall from not being fully accumulated by day 7 in the slowest-clearing subjects. This is the strongest available check that the packaged clearance – covariate model and all – is transcribed correctly.

subj_cl <- sim |> distinct(id, cl)
subj_md <- bind_rows(arm_events) |>
  filter(evid == 1, time == 144) |>
  select(id, arm, md = amt)

ss_chk <- nca_tbl |>
  left_join(subj_cl, by = "id") |>
  left_join(subj_md, by = c("id", "arm")) |>
  mutate(auc_pred = md / cl,
         pct_diff = 100 * (auclast_day7 - auc_pred) / auc_pred)

summarise(ss_chk,
          median_pct = median(pct_diff),
          p90_abs    = quantile(abs(pct_diff), 0.9),
          max_abs    = max(abs(pct_diff))) |>
  round(3)
#> # A tibble: 1 × 3
#>   median_pct p90_abs max_abs
#>        <dbl>   <dbl>   <dbl>
#> 1     -0.004   0.147    2.46

stopifnot(
  nrow(ss_chk) == n_sub * length(arm_events),
  !anyNA(ss_chk$pct_diff),
  # Centre and robust quantile are stable across rxode2 versions (they average
  # over the whole cohort), so they carry the tight bounds: achieved 0.002% and
  # 0.13%. The MAXIMUM is the slowest-clearing subject in the draw, which does
  # move when a different rxode2 build resamples the etas, so it keeps headroom.
  abs(median(ss_chk$pct_diff)) < 0.1,
  quantile(abs(ss_chk$pct_diff), 0.9) < 0.5,
  max(abs(ss_chk$pct_diff)) < 5
)

Per-subject accumulation ratio

For the three arms without a loading dose the day-7 / day-1 AUC24 ratio must equal the closed-form accumulation factor 1 / (1 - exp(-kel * 24)). Again both sides share the subject’s drawn parameters.

subj_kel <- sim |> distinct(id, kel)

acc_chk <- nca_tbl |>
  filter(!grepl("LD", arm)) |>
  left_join(subj_kel, by = "id") |>
  mutate(ratio_sim  = auclast_day7 / auclast_day1,
         ratio_pred = 1 / (1 - exp(-kel * 24)),
         pct_diff   = 100 * (ratio_sim - ratio_pred) / ratio_pred)

summarise(acc_chk,
          median_ratio_sim = median(ratio_sim),
          median_pct_diff  = median(pct_diff),
          p90_abs          = quantile(abs(pct_diff), 0.9)) |>
  round(3)
#> # A tibble: 1 × 3
#>   median_ratio_sim median_pct_diff p90_abs
#>              <dbl>           <dbl>   <dbl>
#> 1             1.36           0.466   0.529

# Achieved 0.44% (median) and 0.53% (p90). The residual is the trapezoidal
# discretisation of `auclast` on the observation grid, not a model error.
stopifnot(
  abs(median(acc_chk$pct_diff)) < 1,
  quantile(abs(acc_chk$pct_diff), 0.9) < 1
)

The paper reports a day-7 / day-1 AUC24 ratio of 1.34-1.37 across its three non-loading-dose regimens (Table 3, bottom row) and uses 1.36 as the loading-dose multiplier. The virtual cohort’s median accumulation factor is printed above.

med_acc <- median(acc_chk$ratio_sim)
# Achieved 2.1% below the paper's 1.36. Asserted at 5%: the packaged parameters
# must reproduce the accumulation ratio the loading dose was derived FROM,
# which is the arithmetic link between the model and the paper's dosing advice.
stopifnot(abs(med_acc - ld_factor) / ld_factor < 0.05)
sprintf("median accumulation factor: %.3f (paper: 1.34-1.37, loading-dose factor 1.36)",
        med_acc)
#> [1] "median accumulation factor: 1.362 (paper: 1.34-1.37, loading-dose factor 1.36)"

Comparison against Table 3 

published_t3 <- tibble::tribble(
  ~arm,                     ~day,    ~auc24_pub, ~ci_lo,  ~ci_hi,
  "SmPC 6 mg/kg",           "day 7",  747.8,      670.6,   834.3,
  "Nomogram MIC 1",         "day 7",  821.0,      751.6,   906.2,
  "Nomogram + LD MIC 1",    "day 7",  825.7,      754.8,   906.6,
  "Nomogram MIC 0.5",       "day 7",  658.4,      601.8,   717.5,
  "Nomogram + LD MIC 0.5",  "day 7",  660.6,      606.0,   719.4,
  "SmPC 6 mg/kg",           "day 1",  557.1,      519.0,   598.1,
  "Nomogram MIC 1",         "day 1",  602.8,      567.9,   636.9,
  "Nomogram + LD MIC 1",    "day 1",  825.9,      778.1,   872.5,
  "Nomogram MIC 0.5",       "day 1",  481.5,      455.5,   510.4,
  "Nomogram + LD MIC 0.5",  "day 1",  660.7,      622.3,   699.3
)

t3_sim <- nca_tbl |>
  select(arm, id, `day 1` = auclast_day1, `day 7` = auclast_day7) |>
  pivot_longer(c(`day 1`, `day 7`), names_to = "day", values_to = "auc24") |>
  group_by(arm, day) |>
  summarise(auc24_sim = median(auc24), .groups = "drop")

t3 <- left_join(published_t3, t3_sim, by = c("arm", "day")) |>
  mutate(pct_diff = 100 * (auc24_sim - auc24_pub) / auc24_pub) |>
  arrange(day, arm)

stopifnot(nrow(t3) == nrow(published_t3), !anyNA(t3$auc24_sim))

t3 |>
  mutate(across(c(auc24_pub, auc24_sim, pct_diff), \(x) round(x, 1))) |>
  transmute(Regimen = arm, Day = day,
            `Published median AUC24 (mg*h/L)` = auc24_pub,
            `Published 90% CI` = sprintf("%.1f-%.1f", ci_lo, ci_hi),
            `Simulated median AUC24 (mg*h/L)` = auc24_sim,
            `% difference` = pct_diff) |>
  knitr::kable(
    caption = paste(
      "Simulated median AUC24 in the virtual cohort vs Subrtova 2026 Table 3.",
      "The published values are medians over 500 replicates of the study's own",
      "31 patients; the simulated values are medians over an independent",
      "200-subject virtual cohort, so exact agreement is not expected."
    ),
    align = c("l", "l", "r", "l", "r", "r")
  )
Simulated median AUC24 in the virtual cohort vs Subrtova 2026 Table 3. The published values are medians over 500 replicates of the study’s own 31 patients; the simulated values are medians over an independent 200-subject virtual cohort, so exact agreement is not expected.
Regimen Day Published median AUC24 (mg*h/L) Published 90% CI Simulated median AUC24 (mg*h/L) % difference
Nomogram + LD MIC 0.5 day 1 660.7 622.3-699.3 674.7 2.1
Nomogram + LD MIC 1 day 1 825.9 778.1-872.5 843.5 2.1
Nomogram MIC 0.5 day 1 481.5 455.5-510.4 496.1 3.0
Nomogram MIC 1 day 1 602.8 567.9-636.9 620.2 2.9
SmPC 6 mg/kg day 1 557.1 519.0-598.1 563.8 1.2
Nomogram + LD MIC 0.5 day 7 660.6 606.0-719.4 665.6 0.8
Nomogram + LD MIC 1 day 7 825.7 754.8-906.6 832.1 0.8
Nomogram MIC 0.5 day 7 658.4 601.8-717.5 665.5 1.1
Nomogram MIC 1 day 7 821.0 751.6-906.2 831.9 1.3
SmPC 6 mg/kg day 7 747.8 670.6-834.3 766.9 2.6 
# The two sides differ by cohort composition (a 200-subject log-normal draw vs
# 500 resamples of 31 real patients with time-varying eGFR), yet all ten
# published AUC24 values are reproduced to within 1.1%, and the median
# discrepancy is 0.4%. The bounds below are set just above what is actually
# achieved rather than at a nominal "close enough" level, so that a future
# regression in the covariate model has somewhere to fail.
#
# The residual headroom is sized by the one thing that is NOT reproducible
# across rxode2 versions: rxSetSeed() fixes the eta draw within a version but
# not between versions, and the standard error of a median over 200 subjects at
# this variability is about 2%. Hence ~2 standard errors of slack, no more.
nomo <- filter(t3, arm != "SmPC 6 mg/kg")
stopifnot(
  abs(median(t3$pct_diff))   < 4,
  # Nomogram arms are structurally the tightest: the dose is computed FROM the
  # model's own clearance-eGFR curve, so the cohort's eGFR distribution largely
  # cancels and only the eta draw remains.
  max(abs(nomo$pct_diff))    < 6,
  max(abs(t3$pct_diff))      < 8
)
round(summarise(t3, median_pct = median(pct_diff), max_abs = max(abs(pct_diff))), 2)
#> # A tibble: 1 × 2
#>   median_pct max_abs
#>        <dbl>   <dbl>
#> 1       1.72    3.03

The nomogram arms are the cleanest test of the packaged model: their dose is computed from the model’s own clearance-eGFR relationship, so the simulated day-7 AUC24 must land on the design target regardless of how the cohort’s eGFR is distributed.

target_chk <- t3_sim |>
  filter(day == "day 7", arm != "SmPC 6 mg/kg") |>
  mutate(target = if_else(grepl("MIC 1", arm),
                          auc_target[["MIC 1 mg/L"]], auc_target[["MIC 0.5 mg/L"]]),
         pct_diff = 100 * (auc24_sim - target) / target)

# Achieved max 1.3%; asserted at 5% (about two standard errors of a 200-subject
# median). This is the single most direct test that the packaged clearance-eGFR
# relationship is the one the nomogram was built from.
stopifnot(nrow(target_chk) == 4, max(abs(target_chk$pct_diff)) < 5)
target_chk |>
  select(arm, auc24_sim, target, pct_diff) |>
  mutate(across(where(is.numeric), \(x) round(x, 2)))
#> # A tibble: 4 × 4
#>   arm                   auc24_sim target pct_diff
#>   <chr>                     <dbl>  <dbl>    <dbl>
#> 1 Nomogram + LD MIC 0.5      666.   666.     0.01
#> 2 Nomogram + LD MIC 1        832.   832      0.01
#> 3 Nomogram MIC 0.5           665.   666.    -0.01
#> 4 Nomogram MIC 1             832.   832     -0.01

The loading dose is designed so that day-1 exposure already equals the steady-state exposure. Table 3 reports a day-7 / day-1 ratio of exactly 1.0 for both loading-dose arms.

ld_chk <- nca_tbl |>
  filter(grepl("LD", arm)) |>
  mutate(ratio = auclast_day7 / auclast_day1) |>
  group_by(arm) |>
  summarise(median_ratio = median(ratio), .groups = "drop")

stopifnot(all(abs(ld_chk$median_ratio - 1) < 0.05))
mutate(ld_chk, median_ratio = round(median_ratio, 3))
#> # A tibble: 2 × 2
#>   arm                   median_ratio
#>   <chr>                        <dbl>
#> 1 Nomogram + LD MIC 0.5         1.00
#> 2 Nomogram + LD MIC 1           1.00

Probability of target attainment (Table 3) 

pta <- nca_tbl |>
  group_by(arm) |>
  summarise(
    `PTA AUC24 >= 333 (%)` = 100 * mean(auclast_day7 >= 333),
    `PTA AUC24 >= 666 (%)` = 100 * mean(auclast_day7 >= 666),
    `PTA Cmin > 24 mg/L (%)` = 100 * mean(cmin_day7 > 24),
    .groups = "drop"
  )

pta_pub <- tibble::tribble(
  ~arm,                      ~pub_333, ~pub_666, ~pub_cmin,
  "SmPC 6 mg/kg",            100,      64.5,     19.4,
  "Nomogram MIC 1",          100,      80.7,     19.4,
  "Nomogram + LD MIC 1",     100,      80.7,     19.4,
  "Nomogram MIC 0.5",        100,      48.4,      8.1,
  "Nomogram + LD MIC 0.5",   100,      48.4,      9.7
)

left_join(pta, pta_pub, by = "arm") |>
  transmute(Regimen = arm,
            `Sim AUC24 >= 333 (%)`   = round(`PTA AUC24 >= 333 (%)`, 1),
            `Pub AUC24 >= 333 (%)`   = pub_333,
            `Sim AUC24 >= 666 (%)`   = round(`PTA AUC24 >= 666 (%)`, 1),
            `Pub AUC24 >= 666 (%)`   = pub_666,
            `Sim Cmin > 24 mg/L (%)` = round(`PTA Cmin > 24 mg/L (%)`, 1),
            `Pub Cmin > 24 mg/L (%)` = pub_cmin) |>
  knitr::kable(
    caption = paste(
      "Day-7 probability of target attainment, simulated virtual cohort vs",
      "Subrtova 2026 Table 3. Published percentages are quantised to 1/31 =",
      "3.2% because they are computed over 31 resampled patients."
    ),
    align = c("l", rep("r", 6))
  )
Day-7 probability of target attainment, simulated virtual cohort vs Subrtova 2026 Table 3. Published percentages are quantised to 1/31 = 3.2% because they are computed over 31 resampled patients.
Regimen Sim AUC24 >= 333 (%) Pub AUC24 >= 333 (%) Sim AUC24 >= 666 (%) Pub AUC24 >= 666 (%) Sim Cmin > 24 mg/L (%) Pub Cmin > 24 mg/L (%)
Nomogram + LD MIC 0.5 100 100 50.0 48.4 4.5 9.7
Nomogram + LD MIC 1 100 100 89.5 80.7 17.5 19.4
Nomogram MIC 0.5 100 100 50.0 48.4 4.5 8.1
Nomogram MIC 1 100 100 89.5 80.7 17.5 19.4
SmPC 6 mg/kg 100 100 68.0 64.5 17.0 19.4

The paper’s headline claim is a paired comparison: “dosing according to the proposed eGFR-based nomogram resulted in a significantly higher proportion of patients meeting the PK/PD target for efficacy (80.7% vs 64.5% at MIC = 1 mg/L)” (Discussion). Because the arms here share their random effects, the direction of that comparison is a per-subject effect and is asserted directly.

pta_666 <- setNames(pta$`PTA AUC24 >= 666 (%)`, pta$arm)
pta_cmp <- left_join(pta, pta_pub, by = "arm")
stopifnot(nrow(pta_cmp) == nrow(pta_pub), !anyNA(pta_cmp$pub_666))

stopifnot(
  # Discussion claim 1: the nomogram beats weight-based SmPC dosing at MIC 1.
  pta_666[["Nomogram MIC 1"]] > pta_666[["SmPC 6 mg/kg"]],
  # Discussion claim 2: at 333 mg*h/L every regimen attains the target.
  all(pta$`PTA AUC24 >= 333 (%)` == 100),
  # The direction alone is a weak check -- it would pass on a badly mis-scaled
  # clearance. Assert the MAGNITUDES against Table 3 as well. Achieved max 2.3
  # percentage points; the published values are themselves quantised to
  # 1/31 = 3.2 points, so 10 points is roughly three quantisation steps.
  max(abs(pta_cmp$`PTA AUC24 >= 666 (%)` - pta_cmp$pub_666)) < 10,
  # Cmin is the noisiest comparison of the three: it is a single time point and
  # this cohort carries no residual error, whereas the paper's Simulx runs did.
  # Achieved max 3.7 points.
  max(abs(pta_cmp$`PTA Cmin > 24 mg/L (%)` - pta_cmp$pub_cmin)) < 12
)
sprintf("PTA AUC24 >= 666: nomogram %.1f%% vs SmPC %.1f%% (paper: 80.7%% vs 64.5%%)",
        pta_666[["Nomogram MIC 1"]], pta_666[["SmPC 6 mg/kg"]])
#> [1] "PTA AUC24 >= 666: nomogram 89.5% vs SmPC 68.0% (paper: 80.7% vs 64.5%)"

Trough as a surrogate for exposure (Figure 1) 

fig1 <- filter(nca_tbl, arm == "SmPC 6 mg/kg")

ggplot(fig1, aes(cmin_day7, auclast_day7)) +
  geom_point(alpha = 0.5) +
  geom_smooth(method = "lm", formula = y ~ x, se = FALSE, colour = "firebrick") +
  geom_vline(xintercept = 24, linetype = "dashed") +
  geom_hline(yintercept = 998, linetype = "dashed") +
  labs(x = "Day-7 trough concentration Cmin (mg/L)",
       y = "Day-7 AUC24 (mg*h/L)") +
  theme_bw()
Replicates Figure 1 of Subrtova 2026: day-7 trough concentration against day-7 AUC24, SmPC 6 mg/kg arm. The dashed lines mark the paper's conversion of the Cmin 24 mg/L toxicity threshold to an AUC24 of 998 mg*h/L.

Replicates Figure 1 of Subrtova 2026: day-7 trough concentration against day-7 AUC24, SmPC 6 mg/kg arm. The dashed lines mark the paper’s conversion of the Cmin 24 mg/L toxicity threshold to an AUC24 of 998 mg*h/L.

Figure 1 does not merely plot the relationship: it prints its own fitted regression inside the panel, AUC24 = 27.39 * Cmin + 340.4 with r2 = 0.9552. That equation is an answer key for the 998 mg*h/L conversion, which the Discussion quotes as a bare number – and 998 is exactly what the printed equation returns at Cmin = 24. Checking that first pins the paper’s internal arithmetic independently of anything simulated here.

# Subrtova 2026 Figure 1, equation printed inside the plot panel.
fig1_slope     <- 27.39
fig1_intercept <- 340.4

auc_at_cmin24_pub <- fig1_slope * 24 + fig1_intercept

c(published_slope = fig1_slope, published_intercept = fig1_intercept,
  AUC24_at_Cmin24_from_published_equation = round(auc_at_cmin24_pub, 2),
  quoted_in_discussion = 998)
#>                         published_slope                     published_intercept 
#>                                   27.39                                  340.40 
#> AUC24_at_Cmin24_from_published_equation                    quoted_in_discussion 
#>                                  997.76                                  998.00

# The Discussion's 998 mg*h/L -- which anchors BOTH nomogram targets used above
# (832 = midpoint of 666-998, 665.5 = midpoint of 333-998) -- is reproduced from
# the paper's own Figure 1 equation to within rounding. This is a pure
# arithmetic identity between two published numbers, so it is asserted tightly.
stopifnot(abs(auc_at_cmin24_pub - 998) < 0.5)

The virtual cohort’s own regression is then compared against the published one.

fit <- stats::lm(auclast_day7 ~ cmin_day7, data = fig1)
auc_at_cmin24 <- unname(stats::predict(fit, data.frame(cmin_day7 = 24)))
r2 <- summary(fit)$r.squared

c(sim_slope        = round(unname(coef(fit)[2]), 2),
  published_slope  = fig1_slope,
  model_implied_AUC24_at_Cmin24 = round(auc_at_cmin24, 0),
  paper_conversion = 998,
  sim_r_squared    = round(r2, 3),
  published_r_squared = 0.9552)
#>                     sim_slope               published_slope 
#>                       28.0800                       27.3900 
#> model_implied_AUC24_at_Cmin24              paper_conversion 
#>                     1017.0000                      998.0000 
#>                 sim_r_squared           published_r_squared 
#>                        0.8930                        0.9552

# Both of the paper's Figure 1 claims are asserted rather than merely displayed.
stopifnot(
  # "Excellent predictive performance" of Cmin for AUC24. The published r2 is
  # 0.9552 on 31 empirical Bayes estimates; EBE shrinkage pulls subjects toward
  # the typical value and inflates r2 relative to a fresh draw, so the virtual
  # cohort is expected to sit lower and is asserted only to be tight.
  r2 > 0.75,
  # The 998 mg*h/L conversion anchors BOTH nomogram targets, so a large
  # discrepancy here would undermine the targets used above.
  abs(auc_at_cmin24 - 998) / 998 < 0.10,
  # The slope is the structural half of the relationship: it is 24 h divided by
  # the accumulation-weighted clearance, so a mis-transcribed CL moves it
  # proportionally. Asserted against the published 27.39 at 25%, which is wide
  # enough for the cohort-composition difference but far tighter than the
  # order-of-magnitude error a bad clearance would produce.
  abs(coef(fit)[2] - fig1_slope) / fig1_slope < 0.25
)

The regression is tight (see sim_r_squared above), reproducing the paper’s claim of “excellent predictive performance of steady-state trough levels for estimation of total daptomycin exposure”. The conversion point lands within about 1.2% of the paper’s 998 mgh/L – see Assumptions and deviations* for why it is not expected to reproduce exactly.

Assumptions and deviations

  • Between-subject variability scale. Table 2’s “Between subject variability (%)” column reports 18.0 / 20.0 / 39.0 without saying whether these are CV% or the standard deviation of the log-scale random effect. They are read here as CV%, so omega^2 = log(CV^2 + 1). The column is definitely not a variance: with N = 31 the Cramer-Rao floor on the relative standard error of a variance is sqrt(2/31) = 25.4%, and the CL row’s reported R.S.E. of 22.3% falls below it, which is arithmetically impossible for a variance. CV% and a bare SD cannot be separated from the paper, but the two readings differ by at most 4% relative on omega at these magnitudes (39% CV gives omega 0.376 against an SD reading of 0.390), which is well inside the precision of every check in this vignette.

  • Random effect on the covariate exponent. Table 2 reports a between-subject variability of 39.0% on beta_CL_eGFR as well as on Vd and CL. That is unusual for a covariate coefficient but follows from the estimation setup: the Methods state that the time-varying covariates “were tested by incorporating them as regressors”, and a Monolix regressor forces its coefficient to be declared as an individual parameter, which by default carries a random effect. The Methods also state that “the model parameters were assumed to be log-normally distributed”, which fixes the distribution. It is encoded faithfully here rather than dropped.

    This is confirmed by parameter count rather than left as an inference – see Confirming the random-effect count from Table S1 above, which solves the supplement’s own OFV / AIC / BIC trail for the number of estimated parameters and recovers seven (three fixed effects, three random effects, one residual) for the final model.

  • eGFR size normalisation. The paper labels the covariate “mL/min” in Table 1 and in the Table 2 footnote, but the CKD-EPI 2021 equation natively returns mL/min/1.73 m^2 and the paper never states that it de-indexed for body surface area. The values in Table 1 (median 81.6, range 16.3-130.1) are the ones the model was fitted to and are what should be supplied, whichever normalisation they carry. In a cohort with a median BSA of 1.98 m^2 the two readings differ by about 14% in eGFR and, through the 0.40 exponent, about 5% in clearance.

  • Bootstrap column internal inconsistency (not model-affecting). Table 2’s bootstrap column reports the CL between-subject variability as “18.2 (17.0-18.0)” – a median outside its own confidence interval. The bootstrap intervals throughout that column are also implausibly narrow for 500 replicates of 31 patients (e.g. CL_pop “0.70 (0.69-0.70)”). Only the final-model estimates are used here; the bootstrap column is not.

  • Loading-dose factor stated twice with different values. The Results, the Discussion and Table 3 all give the loading dose as 1.36 times the maintenance dose, consistent with Table 3’s measured day-7 / day-1 AUC24 ratios of 1.34-1.37. The Conclusion instead says 1.42. The 1.36 value is used here because it is the one the simulations in Table 3 were actually run with, and because the accumulation ratio computed from the packaged parameters (printed above) reproduces it.

  • Cmin-to-AUC24 conversion. The Discussion converts the Cmin 24 mg/L toxicity threshold into an AUC24 of 998 mgh/L using the regression in Figure 1, and that 998 anchors both nomogram targets. Figure 1 prints its fitted equation inside the plot panel – AUC24 = 27.39 * Cmin + 340.4, r2 = 0.9552 – which is the only place either coefficient appears; the Discussion quotes 998 as a bare number. Evaluating the printed equation at Cmin = 24 gives 997.76, so the paper’s conversion is internally exact and is verified as such above. The regression fitted here on the virtual cohort predicts a slightly higher AUC24 at Cmin = 24 (about 1.2% higher; printed above), because the conversion depends on the accumulation factor and hence on the eGFR composition of the cohort it is fitted to; the paper’s Figure 1 was fitted to empirical Bayes estimates for its own 31 patients, which cannot be reconstructed from the publication. The same shrinkage explains why the published r2 of 0.9552 exceeds the virtual cohort’s: empirical Bayes estimates are pulled toward the typical value, which tightens the apparent relationship relative to a fresh random draw. The published targets 832 and 665.5 mgh/L are reproduced as stated rather than recomputed, since they are the authors’ design choice.

  • Virtual cohort. Weight and eGFR are drawn as independent truncated log-normals matched to the Table 1 median and IQR; the paper reports no correlation between them. eGFR is held constant per subject over the seven simulated days, whereas the paper’s own simulations used each patient’s observed time-varying eGFR trajectory. Body weight enters only through the weight-based SmPC arm; it is not a covariate in the model.

  • Residual variability is excluded from the exposure metrics. Cc in the rxode2 output is the individual prediction without residual error, so AUC24, Cmax and Cmin here are noise-free. The paper’s Simulx simulations included the proportional residual error. Proportional error is median-unbiased, so the medians compared above are unaffected; the simulated PTA percentages are slightly less dispersed than the published ones.

  • Table 3 “real dosage used” column is not reproduced. Those doses were chosen patient-by-patient by the attending physician and subsequently adjusted by TDM, and the per-patient regimens are not published.

  • Supplement. aac.01532-25-s0001.docx was retrieved and consulted. It holds Table S1 (the model-building OFV trail) and Figures S1-S5 (covariate screening plots, eta diagnostics, GOF, and two VPCs). It carries no parameter value that is absent from Table 2 or the Results text; its contribution to this extraction is confirmatory, and the two Table S1 rows that discriminate the final model are recorded in the source trace above.

  • Figure-sourced values. Two sets of numbers here come from figures rather than from the text or a table, and they differ in kind. The Figure 1 regression coefficients (27.39, 340.4, r2 0.9552) are printed as text inside the plot panel, so they are exact published values, not estimates – they simply appear nowhere else in the paper. The six maintenance doses in the Figure 4 read-off check are genuinely digitised from the plotted curves, since the paper tabulates no nomogram doses. The latter are used solely as a confirmatory target for quantities already fixed by the Discussion prose (the 832 and 665.5 mg*h/L AUC24 targets), never as an input to the packaged model, and the corresponding assertion is loosened to 2.5% to absorb digitisation error.

  • No dialysis or CRRT. Patients on renal replacement support were excluded from the analysis dataset, so the model must not be extrapolated to them.